Beceiro, BieitoGonzález-Domínguez, JorgeTouriño, Juan2023-05-122023-05-122022-11B. Beceiro, J. González-Domínguez & J. Touriño, "Parallel-FST: A feature selection library for multicore clusters", Journal of Parallel and Distributed Computing, 169, 2022, pp. 106-116. doi: 10.1016/j.jpdc.2022.06.012http://hdl.handle.net/2183/33071Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract]: Feature selection is a subfield of machine learning focused on reducing the dimensionality of datasets by performing a computationally intensive process. This work presents Parallel-FST, a publicly available parallel library for feature selection that includes seven methods which follow a hybrid MPI/multithreaded approach to reduce their runtime when executed on high performance computing systems. Performance tests were carried out on a 256-core cluster, where Parallel-FST obtained speedups of up to 229x for representative datasets and it was able to analyze a 512 GB dataset, which was not previously possible with a sequential counterpart library due to memory constraints.engAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/Feature selectionMutual informationMPIHyperThreadingHigh performance computingjournal articleopen access10.1016/j.jpdc.2022.06.012